Depth imaging system for human posture recognition

Abstract

This paper presents a human posture recognition system based on depth imaging. The proposed system is able to efficiently model human postures by exploiting the depth information captured by an RGB-D camera. Firstly, a skeleton model is used to represent the current pose. Human skeleton configuration is then analyzed in the 3D space to compute joint-based features. Our feature set characterizes the spatial configuration of the body through the 3D joint pairwise distances and the geometrical angles defined by the body segments. Posture recognition is then performed through a supervised classification method. To evaluate the proposed system we created a new challenging dataset with a significant variability regarding the participants and the acquisition conditions. The experimental results demonstrated the high precision of our method in recognizing human postures, while being invariant to several perturbation factors, such as scale and orientation change. Moreover, our system is able to operate efficiently, regardless illumination conditions in an indoor environment, as it is based depth imaging using the infrared sensor of an RGB-D camera.